论文标题
学习人形生物的全身运动技能
Learning Whole-body Motor Skills for Humanoids
论文作者
论文摘要
本文提出了一个深入强化学习的等级框架，该框架获得了运动技能，以进行各种推动恢复和平衡行为，即踝关节，臀部，脚倾斜和踩踏策略。该政策经过物理模拟器的培训，具有机器人模型和低级阻抗控制的现实设置，这些设置易于将学习的技能传输到真正的机器人。与传统方法相比，优势是将高级计划者和反馈控制的整合在一个单一的连贯策略网络中，这对于学习多功能平衡和恢复动作是一般性的，反对在任意位置（例如腿，躯干）的未知扰动。此外，拟议的框架允许许多最新的学习算法快速学习策略。通过将我们所学到的结果与文献中的预编程，特殊用途控制器的研究进行比较，自学技能在干扰拒绝方面是可比的，但具有产生各种适应性，多功能和强大行为的其他优势。
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i.e., ankle, hip, foot tilting, and stepping strategies. The policy is trained in a physics simulator with realistic setting of robot model and low-level impedance control that are easy to transfer the learned skills to real robots. The advantage over traditional methods is the integration of high-level planner and feedback control all in one single coherent policy network, which is generic for learning versatile balancing and recovery motions against unknown perturbations at arbitrary locations (e.g., legs, torso). Furthermore, the proposed framework allows the policy to be learned quickly by many state-of-the-art learning algorithms. By comparing our learned results to studies of preprogrammed, special-purpose controllers in the literature, self-learned skills are comparable in terms of disturbance rejection but with additional advantages of producing a wide range of adaptive, versatile and robust behaviors.
